Overview of current state of research on the application of artificial intelligence techniques for COVID-19

Kumar, Vijay; Singh, Dilbag; Kaur, Manjit; Damaševičius, Robertas

doi:10.7717/peerj-cs.564

Cited by 43 publications

(29 citation statements)

References 148 publications

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“…Several overviews into the application of CNN techniques to aid in COVID-19 diagnosis have been conducted and we refer the reader to Refs. [ [35] , [36] , [37] ] for more details.…”

Section: Related Workmentioning

confidence: 99%

Indirect supervision applied to COVID-19 and pneumonia classification

Danilov

Proutski²,

Karpovsky

et al. 2022

Informatics in Medicine Unlocked

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The novel coronavirus 19 (COVID-19) continues to have a devastating effect around the globe, leading many scientists and clinicians to actively seek to develop new techniques to assist with the tackling of this disease. Modern machine learning methods have shown promise in their adoption to assist the healthcare industry through their data and analytics-driven decision making, inspiring researchers to develop new angles to fight the virus. In this paper, we aim to develop a CNN-based method for the detection of COVID-19 by utilizing patients' chest X-ray images. Developing upon the inclusion of convolutional units, the proposed method makes use of indirect supervision based on Grad-CAM. This technique is used in the training process where Grad-CAM's attention heatmaps support the network's predictions. Despite recent progress, scarcity of data has thus far limited the development of a robust solution. We extend upon existing work by combining publicly available data across 5 different sources and carefully annotate the comprising images across three categories: normal, pneumonia, and COVID-19. To achieve a high classification accuracy, we propose a training pipeline based on indirect supervision of traditional classification networks, where the guidance is directed by an external algorithm. With this method, we observed that the widely used, standard networks can achieve an accuracy comparable to tailor-made models, specifically for COVID-19, with one network in particular, VGG-16, outperforming the best of the tailor-made models.

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“…Several overviews into the application of CNN techniques to aid in COVID-19 diagnosis have been conducted and we refer the reader to Refs. [ [35] , [36] , [37] ] for more details.…”

Section: Related Workmentioning

confidence: 99%

Indirect supervision applied to COVID-19 and pneumonia classification

Danilov

Proutski²,

Karpovsky

et al. 2022

Informatics in Medicine Unlocked

View full text Add to dashboard Cite

show abstract

“…Many automatic techniques are used for solving different problems. AI systems are becoming the core of many real-time and data science-related solutions, including those for COVID-19 [19,20]. Machine learning (ML) methods can be found in science, technology, health care, manufacturing, education, policing, and marketing [21].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

et al. 2021

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Chest diseases can be dangerous and deadly. They include many chest infections such as pneumonia, asthma, edema, and, lately, COVID-19. COVID-19 has many similar symptoms compared to pneumonia, such as breathing hardness and chest burden. However, it is a challenging task to differentiate COVID-19 from other chest diseases. Several related studies proposed a computer-aided COVID-19 detection system for the single-class COVID-19 detection, which may be misleading due to similar symptoms of other chest diseases. This paper proposes a framework for the detection of 15 types of chest diseases, including the COVID-19 disease, via a chest X-ray modality. Two-way classification is performed in proposed Framework. First, a deep learning-based convolutional neural network (CNN) architecture with a soft-max classifier is proposed. Second, transfer learning is applied using fully-connected layer of proposed CNN that extracted deep features. The deep features are fed to the classical Machine Learning (ML) classification methods. However, the proposed framework improves the accuracy for COVID-19 detection and increases the predictability rates for other chest diseases. The experimental results show that the proposed framework, when compared to other state-of-the-art models for diagnosing COVID-19 and other chest diseases, is more robust, and the results are promising.

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“…The proposed model achieved a correlation of 0.9763 correlation on China's COVID-19 outbreak data. For more related works, we would like to refer the readers to the review papers [35,36].…”

Section: Review Of Related Workmentioning

confidence: 99%

Predicting COVID-19 Cases in South Korea with All K-Edited Nearest Neighbors Noise Filter and Machine Learning Techniques

et al. 2021

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The application of machine learning techniques to the epidemiology of COVID-19 is a necessary measure that can be exploited to curtail the further spread of this endemic. Conventional techniques used to determine the epidemiology of COVID-19 are slow and costly, and data are scarce. We investigate the effects of noise filters on the performance of machine learning algorithms on the COVID-19 epidemiology dataset. Noise filter algorithms are used to remove noise from the datasets utilized in this study. We applied nine machine learning techniques to classify the epidemiology of COVID-19, which are bagging, boosting, support vector machine, bidirectional long short-term memory, decision tree, naïve Bayes, k-nearest neighbor, random forest, and multinomial logistic regression. Data from patients who contracted coronavirus disease were collected from the Kaggle database between 23 January 2020 and 24 June 2020. Noisy and filtered data were used in our experiments. As a result of denoising, machine learning models have produced high results for the prediction of COVID-19 cases in South Korea. For isolated cases after performing noise filtering operations, machine learning techniques achieved an accuracy between 98–100%. The results indicate that filtering noise from the dataset can improve the accuracy of COVID-19 case prediction algorithms.

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Overview of current state of research on the application of artificial intelligence techniques for COVID-19

Cited by 43 publications

References 148 publications

Indirect supervision applied to COVID-19 and pneumonia classification

Indirect supervision applied to COVID-19 and pneumonia classification

A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

Predicting COVID-19 Cases in South Korea with All K-Edited Nearest Neighbors Noise Filter and Machine Learning Techniques

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